This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network.
Presentation of a simulation software for spatial model neurons and their networks designed primarily for GPUs.
Presentation of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.
Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.
The lecture covers a brief introduction to neuromorphic engineering, some of the neuromorphic networks that the speaker has developed, and their potential applications, particularly in machine learning.
Introductory presentation on how data science can help with scientific reproducibility.
This lecture covers describing and characterizing an input-output relationship.
Part 1 of 2 of a tutorial on statistical models for neural data
Part 2 of 2 of a tutorial on statistical models for neural data.
Introduction to stability analysis of neural models
Oscillations and bursting
Weakly coupled oscillators
Continuation of coupled oscillators
Firing rate models.
Pattern generation in visual system hallucinations.